CN111445052B

CN111445052B - Vehicle information processing method and device and electronic equipment

Info

Publication number: CN111445052B
Application number: CN201910044427.4A
Authority: CN
Inventors: 裴成
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2019-01-17
Filing date: 2019-01-17
Publication date: 2023-06-09
Anticipated expiration: 2039-01-17
Also published as: CN111445052A

Abstract

The invention discloses a vehicle information processing method, a device and electronic equipment, wherein the processing method comprises the following steps: acquiring vehicle information and a train number task allocated to the vehicle; obtaining a selected feature vector, wherein the feature vector comprises a plurality of features affecting mileage of the vehicle to perform the train number task, and the plurality of features comprises a vehicle feature and a train number feature; obtaining a mapping function between the feature vector and the driving mileage; obtaining the predicted mileage of the vehicle for executing the train number task according to the mapping function and the vector value of the feature vector; and determining whether the vehicle executes the processing result of the train number task according to the predicted mileage.

Description

Vehicle information processing method and device and electronic equipment

Technical Field

The present invention relates to the field of vehicle information processing technology, and more particularly, to a vehicle information processing method, a vehicle information processing apparatus, an electronic device, and a computer-readable storage medium.

Background

With the rapid development of urban transportation, public transportation plays an increasingly important role in passenger flow transportation, and has also put higher demands on the application. For example, for a public vehicle, after the public vehicle finishes the operation plan of the current day and returns to the yard, the public vehicle in the return warehouse needs to be configured so as to meet the tomorrow operation plan.

Generally, after selecting the train number list of the second day, a vehicle with good condition needs to be selected to serve as the corresponding train number task in the list, which is called as the planning and configuration of the operation day of the vehicle. In the prior art, a manual compiling mode is still adopted for planning, namely, overhaul scheduling is matched according to the subjective of vehicle states, parking positions and the like, and the mileage corresponding to different train number tasks is often different, so that the running mileage in the operation process of the vehicle is also greatly different, the reliability of the vehicle with overlarge running mileage is lower than a threshold value when the planned overhaul date is reached, the running unsafe coefficient is increased, and for the vehicle with smaller running mileage, the frequent planned overhaul can cause overhaul resource waste.

Therefore, there is a need to provide a new method of operating and orchestrating a vehicle, and the number of tasks assigned to that vehicle, such that the vehicle's mileage is performed in the direction of the planned maintenance date.

Disclosure of Invention

An object of the embodiment of the invention is to provide a new technical scheme for processing vehicle information.

According to a first aspect of the present invention, there is provided a vehicle information processing method including:

Acquiring vehicle information and a train number task allocated to the vehicle;

obtaining a selected feature vector, wherein the feature vector comprises a plurality of features affecting mileage of a vehicle to perform the train number task, the plurality of features comprising a vehicle feature and a train number feature;

obtaining a mapping function between the feature vector and the driving mileage;

obtaining the predicted mileage of the vehicle for executing the train number task according to the mapping function and the vector value of the feature vector;

and obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage.

Optionally, the vehicle characteristic includes at least one of a vehicle number and a vehicle type; and/or the train number characteristics include at least one of departure time, arrival time, departure location, and arrival location.

Optionally, the plurality of features further includes a cross feature, wherein the cross feature is a feature that cross-correlates a vehicle with the train number task, the cross feature including a service task; the overhaul task comprises at least one of the following: inspection tasks, repair tasks, maintenance tasks, cleaning tasks.

Optionally, the step of obtaining a mapping function between the feature vector and the driving range includes:

Acquiring training samples according to historical operation data, wherein each training sample comprises matched vehicles and actually executed train number tasks;

and training to obtain the mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

Optionally, the step of training to obtain the mapping function includes:

taking undetermined coefficients of the mapping function as variables, and determining a mileage prediction expression of each training sample according to the vector value of the feature vector of each training sample;

constructing a loss function according to the mileage prediction expression of each training sample and the actual mileage of each training sample;

and determining the undetermined coefficient according to the loss function, and completing the training of the mapping function.

Optionally, the step of constructing the loss function includes:

for each training sample, determining a corresponding loss expression according to the mileage prediction expression and the actual mileage;

and summing the loss expression of each training sample to obtain the loss function.

Optionally, the processing method further includes:

After the vehicle executes the train number task, taking the vehicle and the train number task as new training samples;

and correcting the mapping function according to the vector value of the characteristic vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

Optionally, the processing method further includes:

and executing the step of training the mapping function according to a preset training period.

Optionally, the step of obtaining a processing result of whether the vehicle executes the train number task according to the predicted mileage includes:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

and under the condition that the difference value is smaller than or equal to a preset mileage threshold value, obtaining a processing result of the vehicle executing the train number task.

Optionally, the step of obtaining the target mileage of the vehicle includes:

acquiring the maintenance plan mileage of the vehicle and the current actual mileage of the vehicle;

acquiring the running days of the current date from the overhaul date of the vehicle; the method comprises the steps of,

and obtaining the target mileage of the vehicle according to the maintenance plan mileage, the current actual mileage and the running days.

Optionally, the vehicle information processing method further includes:

detecting whether an event of processing vehicle information occurs;

and executing the steps of acquiring vehicle information and the train number tasks allocated to the vehicle when the event occurs.

Optionally, the event includes at least any one or more of the following:

reaching the preset treatment time;

processing operation of receiving externally triggered vehicle information;

and receiving a processing instruction of the vehicle information sent by the terminal equipment.

Optionally, the vehicle information processing method further includes:

and displaying the processing result so as to carry out vehicle configuration.

According to a second aspect of the present invention, there is provided a processing apparatus of vehicle information, comprising:

the vehicle task acquisition module is used for acquiring vehicle information and vehicle number tasks distributed for the vehicle;

a feature vector acquisition module configured to acquire a selected feature vector, where the feature vector includes a plurality of features that affect mileage of a vehicle performing the train number task, the plurality of features including a vehicle feature and a train number feature;

the mapping function acquisition module is used for acquiring a mapping function between the feature vector and the driving mileage;

The mileage predicting module is used for obtaining the predicted mileage of the vehicle for executing the train number task according to the mapping function and the vector value of the feature vector; the method comprises the steps of,

and the execution determining module is used for obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage.

Optionally, the mapping function obtaining module is further configured to:

Optionally, the training to obtain the mapping function includes:

Optionally, the constructing the loss function includes:

Optionally, the processing device further includes:

the new sample determining module is used for taking the vehicle and the train number task as new training samples after the vehicle executes the train number task;

and the function correction module is used for correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

Optionally, the mapping function obtaining module is further configured to:

and training the mapping function according to a preset training period.

Optionally, the execution determining module is further configured to:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

Optionally, the acquiring the target mileage of the vehicle includes:

acquiring the running days of the current date from the overhaul date of the vehicle;

Optionally, the vehicle information processing apparatus further includes:

the event detection module is used for detecting whether an event for processing vehicle information occurs or not;

the vehicle task acquisition module is used for acquiring vehicle information and vehicle number tasks distributed to a vehicle under the condition that the event detection module detects the occurrence of the event.

Optionally, the event includes at least any one or more of the following:

reaching the preset treatment time;

processing operation of receiving externally triggered vehicle information;

Optionally, the vehicle information processing apparatus further includes:

and the display module is used for displaying the processing result so as to carry out vehicle configuration.

According to a third aspect of the present invention, there is also provided an electronic device comprising the processing apparatus according to the second aspect of the present invention; alternatively, the system comprises a memory for storing executable instructions and a processor; the processor is configured to execute the processing method according to any one of the first aspects of the present invention according to control of the instruction.

According to a fourth aspect of the present invention there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a processing method according to any of the first aspects of the present invention.

The method, the device and the electronic equipment have the advantages that after the vehicle information and the vehicle number of the vehicle distributed task are obtained, the mapping function between the feature vector and the driving mileage can be obtained by obtaining the selected feature vector, and the predicted mileage of the vehicle for executing the vehicle number of the vehicle task is obtained according to the mapping function and the vector value of the feature vector, so that the processing result of whether the vehicle executes the vehicle number of the vehicle task is obtained according to the predicted mileage. And further, the driving mileage of the vehicle can be executed according to the direction of planned maintenance, so that the mileage controllability of the vehicle is realized.

Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a block diagram of one example of a hardware configuration of an electronic device that may be used to implement an embodiment of the invention;

FIG. 2 is a block diagram of another example of a hardware configuration of an electronic device that may be used to implement an embodiment of the invention;

fig. 3 is a flowchart of a method of processing vehicle information provided according to a first embodiment of the present invention;

fig. 4 is a flowchart of a method for processing vehicle information according to a second embodiment of the present invention;

fig. 5 is a flowchart of a method for processing vehicle information according to a third embodiment of the present invention;

fig. 6 is a flowchart of a processing method of vehicle information provided according to a fourth embodiment of the present invention;

fig. 7 is a flowchart of a processing method of vehicle information provided according to a fifth embodiment of the present invention;

fig. 8 is a flowchart showing an example of a vehicle information processing method according to an embodiment of the present invention;

Fig. 9 is a schematic block diagram of a vehicle information processing apparatus according to an embodiment of the present invention;

fig. 10 is a functional block diagram of an electronic device provided according to a first embodiment of the present invention;

fig. 11 is a schematic diagram of a hardware structure of an electronic device according to a second embodiment of the present invention.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.

The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.

< hardware configuration >

Fig. 1 and 2 are block diagrams of hardware configurations of an electronic device 1000 that may be used to implement the processing method of any embodiment of the invention.

In one embodiment, as shown in FIG. 1, electronic device 1000 may be a server 1100.

The server 1100 provides the service points for processing, databases, communication facilities. The server 1100 may be a monolithic server or a distributed server across multiple computers or computer data centers. The server may be of various types such as, but not limited to, a web server, news server, mail server, message server, advertisement server, file server, application server, interaction server, database server, or proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported by or implemented by the server. For example, a server, such as a blade server, cloud server, etc., or may be a server group consisting of multiple servers, may include one or more of the types of servers described above, etc.

In this embodiment, the server 1100 may include a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, and an input device 1160, as shown in fig. 1.

In this embodiment, the server 1100 may also include a speaker, microphone, etc., without limitation.

The processor 1110 may be a dedicated server processor, or may be a desktop processor, a mobile processor, or the like that meets performance requirements, which is not limited herein. The memory 1120 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 1130 includes, for example, various bus interfaces such as a serial bus interface (including a USB interface), a parallel bus interface, and the like. The communication device 1140 can perform wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display, an LED display touch display, or the like. The input device 1160 may include, for example, a touch screen, a keyboard, and the like.

In this embodiment, the memory 1120 of the server 1100 is used to store instructions for controlling the processor 1110 to operate to perform at least the method of processing vehicle information according to any embodiment of the present invention. The skilled person can design instructions according to the disclosed solution. How the instructions control the processor to operate is well known in the art and will not be described in detail here.

Although a plurality of devices of the server 1100 are shown in fig. 1, the present invention may relate to only some of the devices, for example, the server 1100 may relate to only the memory 1120 and the processor 1110.

In one embodiment, the electronic device 1000 may be a terminal device 1200 such as a PC, a notebook computer, etc. used by an operator, which is not limited herein.

In this embodiment, referring to fig. 2, the terminal apparatus 1200 may include a processor 1210, a memory 1220, an interface device 1230, a communication device 1240, a display device 1250, an input device 1260, a speaker 1270, a microphone 1280, and the like.

Processor 1210 may be a mobile version processor. The memory 1220 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 1230 includes, for example, a USB interface, a headphone interface, and the like. The communication device 1240 may be, for example, a wired or wireless communication device, and the communication device 1240 may include a short-range communication device, for example, any device that performs short-range wireless communication based on a short-range wireless communication protocol such as a Hilink protocol, wiFi (IEEE 802.11 protocol), mesh, bluetooth, zigBee, thread, Z-Wave, NFC, UWB, liFi, or the like, and the communication device 1240 may also include a remote communication device, for example, any device that performs WLAN, GPRS, 2G/3G/4G/5G remote communication. The display device 1250 is, for example, a liquid crystal display, a touch display, or the like. The input device 1260 may include, for example, a touch screen, a keyboard, and the like. A user may input/output voice information through the speaker 1270 and the microphone 1280.

In this embodiment, the memory 1220 of the terminal device 1200 is used to store instructions for controlling the processor 1210 to operate to perform at least the processing method of vehicle information according to any embodiment of the present invention. The skilled person can design instructions according to the disclosed solution. How the instructions control the processor to operate is well known in the art and will not be described in detail here.

Although a plurality of devices of the terminal apparatus 1200 are shown in fig. 2, the present invention may relate to only some of the devices, for example, the terminal apparatus 1200 may relate to only the memory 1220 and the processor 1210 and the display device 1250.

< method example >

Fig. 3 is a flow chart of a method of processing vehicle information, which may be implemented by an electronic device, according to an embodiment of the present invention. The electronic device may be a server 1100 as shown in fig. 1 or a terminal device 1200 as shown in fig. 2.

As shown in fig. 3, the vehicle information processing method of the present embodiment may include the following steps S3100 to S2400:

step S3100 obtains vehicle information and a train number task assigned to the vehicle.

The vehicle in this embodiment may be a vehicle that travels along a predetermined route, such as a bus, subway, light rail, train, or the like. Specifically, the vehicle can be any vehicle which performs the task to return to the parking lot in the evening before the operation day to be compiled. For example, the operational day to be orchestrated is 2018.12.14, and the vehicle collection may include any vehicle that performs its task at 2018.12.13 pm back to the yard.

The train number task may be one of the tasks assigned to the vehicle in the train number list of the operation day schedule to be orchestrated.

In this step S3100, vehicle information and a train number task assigned to the vehicle may be acquired from a terminal device at an operation site. Specifically, the operator may input the vehicle information and the train number task allocated to the vehicle into the terminal device. The processing method of the embodiment may be executed by the operator entering the train number list and the yard line list into the terminal device, then selecting one of the vehicles by the electronic device, acquiring information of the vehicle, and allocating a train number task to the vehicle.

In this embodiment, the terminal device on the operation site may be the same as or different from the electronic device executing the processing method of this embodiment.

In one embodiment, the processing method may further include: whether an event processing vehicle information occurs is detected, and if the event occurs, the step of step S3100 is executed.

Specifically, the event may include any one or more of the following:

reaching the preset treatment time;

processing operation of receiving externally triggered vehicle information;

In embodiments where the event includes reaching a preset processing time, the preset processing time may be set according to an application scenario or specific requirements. For example, the preset treatment time may be 5 a.m. a day. Then, the treatment method of the present invention may be performed at 5 a.m. per day.

In embodiments where the event includes a processing operation that receives externally triggered vehicle information, the processing operation of the vehicle information may be triggered by an operator directly on the electronic device executing the processing method of the present embodiment. The electronic device may execute the processing method of the present invention upon receiving the processing operation of the vehicle information.

In an embodiment where the event includes receiving a processing instruction of the vehicle information sent by the terminal device, it may be that an operator performs a processing operation on the terminal device, and triggers the terminal device to send the processing instruction of the vehicle information to the electronic device that performs the processing method of the present embodiment. The electronic device may perform the processing method of the present invention upon receiving the processing execution.

In step S3200, a selected feature vector is obtained, where the feature vector includes a plurality of features affecting mileage of a vehicle performing the train number task, and the plurality of features includes a vehicle feature and a train number feature.

The feature vector X includes a plurality of features X that influence mileage of a vehicle to perform a train number task _j The value of j is a natural number from 1 to n, and n represents the total number of features of the feature vector X.

The plurality of features x _j Vehicle characteristics and train number characteristics may be included.

The vehicle characteristic may be determined from vehicle information. The vehicle characteristic may be at least one of a vehicle number and a vehicle type. The vehicle number may be comprised of numbers, letters, and/or words, etc. For example, the vehicle number may be a license plate number.

The train number feature may be at least one of a departure time, an arrival time, a departure location, and an arrival location.

In this embodiment, x _j The characteristic may be a characteristic such as a vehicle characteristic, a vehicle number characteristic, or the like capable of affecting the mileage of the vehicle to perform the vehicle number task, for example, the vehicle number characteristic may be a vehicle number and a vehicle type, the vehicle number characteristic may be a departure time, an arrival time, a departure place, and an arrival place, the feature vector X may have 6 characteristics, that is, n=6, and the feature vector X may be expressed as x= (X ₁ ,x ₂ ,x ₃ ,x ₄ ,x ₅ ,x ₆ ). Of course, other features related to vehicle orchestration may also be included in feature vector X.

The above other feature may be a cross feature, which is a feature that cross-correlates the vehicle with the assigned train number task. The cross-over feature includes a service task that may include at least one of a inspection task, a repair task, a maintenance task, a cleaning task. For example, the vehicle may be subjected to a minor repair operation on the operation date to be assembled, a water washing operation on the vehicle on the operation date to be assembled, a wheel grinding operation on the vehicle on the operation date to be assembled, or the like, which is not limited herein.

In step S3300, a mapping function between the feature vector and the driving range is obtained.

The independent variable of the mapping function F (X) is the eigenvector X, and the dependent variable F (X) is the prediction mileage determined by the eigenvector X.

In this embodiment, the step S3300 of obtaining the mapping function between the feature vector and the driving range may further include steps S3310 to S3320 shown in fig. 4:

step S3310, obtaining training samples according to the historical operating data.

Each training sample includes the paired vehicle and the actual number of vehicle tasks performed.

Step S3320, training to obtain a mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

In one embodiment, steps S3310 to S3320 of the training mapping function may be performed according to a preset training period. The training period may be set according to a specific application scenario or application requirement, for example, may be set to 1 day.

In this embodiment, the mapping function F (x) may be obtained by various fitting means based on the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample, for example, the mapping function F (x) may be obtained by using an arbitrary multiple linear regression model, which is not limited herein.

In one example, the multiple linear regression model may be a simple polynomial function reflecting the mapping function F (x), where each order coefficient of the polynomial function is unknown, and each order coefficient of the polynomial function may be determined by substituting the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample into the polynomial function, so as to obtain the mapping function F (x).

In another example, various regression models, such as an addition model, may be used, where the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample are used as accurate samples to perform multiple rounds of training, each round of training learns the residuals after the previous round of fitting, and the residuals may be controlled to very low values by iterating the T rounds, so that the finally obtained mapping function F (x) has very high accuracy. The addition model is, for example, lightGBM, GBDT, XGBoost, and the like, and is not limited thereto.

In one embodiment, as shown in fig. 5, the training to obtain the mapping function in the above step S3320 may further include the following steps S3321 to S3323:

in step S3321, the mileage prediction expression of each training sample is determined according to the vector value of the feature vector of each training sample by using the undetermined coefficient of the mapping function as a variable.

Assume that feature vector X in the mapping function includes n features X ₁ ,x ₂ ,......,x _n In determining the value of the kth training sample for the n features

Then, a constant weight b and n characteristic weights a are included in the undetermined coefficient set ₁ ,a ₂ ,......,a _n As a variable, the kth training sample mileage prediction expression can be obtained to be Y _k :

In step S3322, a loss function is constructed according to the mileage prediction expression of each training sample and the actual mileage of each training sample.

In this embodiment, the construction of the loss function in the step S3322 may further include steps S3322-1 to S3322-2 as shown in fig. 6:

in step S3322-1, for each training sample, a corresponding loss expression is determined based on the mileage predicted expression and the actual mileage.

Assuming that the number of collected training samples is m, for the kth training sample, the actual mileage obtained is y _k Mileage predictive expression is given as Y _k The corresponding loss expression is (y _k -Y _k ) ² (k=1,., m); wherein, the liquid crystal display device comprises a liquid crystal display device,

step S3322-2, summing the loss expressions of each training sample to obtain a loss function.

In this embodiment, the loss function is:

wherein, the liquid crystal display device comprises a liquid crystal display device,

and step S3323, determining undetermined coefficients according to the loss function, and finishing the training of the mapping function.

In this embodiment, determining the undetermined coefficient according to the loss function in the step S3323, completing the training of the mapping function may further include steps S3323-1 to S3323-3:

step S3323-1, setting constant weights in the undetermined coefficient set and initial values of each characteristic weight as random numbers in a preset numerical range.

Assuming a set of undetermined coefficients { b, a ₁ ,a ₂ ,......,a _n Comprises a constant weight b and n characteristic weights a ₁ ,a ₂ ,......,a _n The initial value may be set to a random number of a preset numerical range. The preset value range can be set according to the application scenario or the application requirement, for example, the preset value range is set to 0-1, so that the constant weight b and the n feature weights a ₁ ,a ₂ ,......,a _n The initial values of (a) are random numbers between 0 and 1.

And S3323-2, substituting the constant weight and each characteristic weight after setting the initial value into the loss function, and performing iterative processing.

In this embodiment, the step S3323-2 of substituting the constant weight after the initial value is set and each characteristic weight into the loss function, and the iterative process may further include the following steps S3323-21 to S3323-22:

step S3323-21, for each constant weight and each characteristic weight, obtaining the corresponding constant weight or the value of the characteristic weight after iteration according to the constant weight or the value of the characteristic weight before the iteration, the convergence parameter and the loss function substituted into the undetermined coefficient set before the iteration.

The convergence parameter is a relevant parameter for controlling the convergence speed of the iterative process, and can be set according to the application scenario or the application requirement, for example, set to 0.01.

And step S3323-22, obtaining a set of undetermined coefficients after the iteration according to the constant weights and the values of each characteristic weight after the iteration.

Assuming that the iteration is the (k+1) th iteration (the initial value of k is 0, and 1 is added with each iteration), the undetermined coefficient set after the iteration is { b, a ₁ ,a ₂ ,...,a _n } ^(k+1) 。

And step S3323-3, terminating the iterative process when the undetermined coefficient set obtained by the iterative process accords with the convergence condition, determining the constant weight of the undetermined coefficient set and the value of each characteristic weight, and if not, continuing the iterative process.

The convergence condition can be set according to specific application scenarios or application requirements.

For example, the convergence condition is that the number of iterative processes is greater than a preset number of times threshold. The preset number of times threshold may be set according to engineering experience or experimental simulation results, for example, may be set to 300. Correspondingly, assuming that the number of iterative processes is k+1, the number of times threshold is itemNums, and the corresponding convergence condition is: k is larger than or equal to itemNams.

For another example, the convergence condition is that an iteration result value of the undetermined coefficient set obtained by the iteration process is smaller than a preset result threshold value. The iteration result value is determined according to the loss function substituted by the undetermined coefficient set obtained through the iteration process and the corresponding constant weight or the result of the bias derivative of each characteristic weight.

In one example, the convergence condition is that any one of the above two examples is satisfied, and a specific convergence condition is described in the above two examples, which is not described herein.

Let k+1th iteration process get undetermined coefficient set { b, a ₁ ,a ₂ ,...,a _n } ^(k+1) When the convergence condition is met, stopping the iterative processing to obtain corresponding all a _i ^(k+1) (i=1,., n) and b ^(k+1) And if not, continuing the iterative processing until the undetermined coefficient set meets the convergence condition.

According to the embodiment of the invention, the mapping function can be obtained according to a large number of training samples, so that the accuracy of the obtained predicted mileage can be improved when the predicted mileage is determined by using the mapping function.

And S3400, obtaining the predicted mileage of the vehicle for executing the train number task according to the mapping function and the vector value of the feature vector.

The vector value may specifically be a value of the feature vector.

In this embodiment, according to step S3300, a mapping function between the feature vector and the driving range is obtained, and according to the vector value of the feature vector, the vector value is substituted into the mapping function F (x) so as to obtain the predicted mileage of the vehicle for executing the train number task.

According to the embodiment of the invention, the predicted mileage of the vehicle for executing the train number task can be obtained according to the feature vector and the mapping function, and the accuracy of the obtained predicted mileage can be improved when the predicted mileage is determined by using the mapping function because the mapping function is obtained by training a large number of training samples.

Step S3500, obtaining a processing result of whether the vehicle executes the train number task according to the predicted mileage.

In one embodiment, the step S3500 may further include steps S3510 to S3530 as shown in fig. 7:

In step S3510, a target mileage of the vehicle is obtained.

The target mileage of the vehicle may be a planned daily mileage of the vehicle, and when the vehicle runs according to the target mileage, the running of the vehicle can be performed in a planned maintenance direction, that is, when a specified maintenance date is reached, the vehicle has basically run after the maintenance planning mileage.

The maintenance schedule mileage of the vehicle is the travel mileage of the vehicle specified when the maintenance date of the vehicle is reached. For example, the specified maintenance date is 2018.12.30, the maintenance schedule mileage of the vehicle is 10000 km, and when the vehicle is traveling, if traveling in accordance with the target mileage, it is possible to make the vehicle travel substantially 10000 km already when the specified maintenance date 2018.12.30 arrives.

In one embodiment, obtaining the target mileage of the vehicle may further include the following steps S3511 to S3513:

in step S3511, the maintenance schedule mileage of the vehicle and the current actual mileage of the vehicle are obtained.

The maintenance schedule mileage of the vehicle is a travel mileage of the vehicle specified when the maintenance date of the vehicle is reached. For example, the specified service date is 2018.12.30 and the vehicle has a service plan mileage of 10000 km, i.e., when the specified service date 2018.12.30 is reached, it is necessary to ensure that the vehicle has traveled substantially 10000 km.

The current actual mileage of the vehicle is the total mileage of the vehicle before the operation day to be scheduled. Taking 2018.12.14 as an example, the current actual mileage of the vehicle obtained according to step S4100 may be 2000 km, i.e. the total travel mileage of the vehicle before 2018.12.14 (excluding 2018.12.14) is 2000 km.

In one example, the terminal device on site actively transmits the current actual mileage of the vehicle to the electronic device executing the method of the present invention after the vehicle has performed the task and returned to the yard.

In one example, the electronic device may request the terminal device on site to acquire the current actual mileage of the vehicle after responding to the processing request for the vehicle and the train number task.

Step S3512, obtaining the running days of the current date from the overhaul date of the vehicle.

Still taking 2018.12.30 as the specified overhaul date, 2018.12.14 as the operation date to be compiled, 10000 km as the overhaul plan mileage of the vehicle is obtained according to step S3511, 2000 km as the current actual mileage of the vehicle, and 16 days as the operation date from the overhaul date of the vehicle is obtained according to step S3512.

Step S3513, obtaining the target mileage of the vehicle according to the maintenance schedule mileage, the current actual mileage and the operation days.

In this embodiment, the calculation formula of the target mileage of the vehicle is as follows:

wherein M represents the target mileage of the vehicle, b ₂ Representing the mileage of a vehicle in a maintenance plan, b ₁ Representing the current actual mileage of the vehicle, day represents the number of days of operation of the current date from the date of service of the vehicle.

Still taking the specified overhaul date as 2018.12.30 and the operation date to be compiled as 2018.12.14 as an example, obtaining the overhaul plan mileage of the vehicle as 10000 km according to step S3513, and the current actual mileage of the vehicle as 2000 km, obtaining the operation date of the current date as 16 days from the overhaul date of the vehicle according to step S3512, and obtaining the target mileage of the vehicle as 16 days according to step S3513

I.e. the target mileage of the vehicle is 500 km, it is also understood that the planned travel mileage of the vehicle at 2018.12.14 is 500 kmAfter the vehicle finishes the train number task 2018.12.14 according to the configuration result, when 2018.12.15 is configured, adding the mileage of the vehicle actually operated by the train number task 2018.12.14, and re-calculating the target mileage of the vehicle on 2018.12.15 days to improve the accuracy of the target mileage of the vehicle on each operation day, so that the current actual mileage gradually converges to the maintenance plan mileage when the vehicle reaches the set maintenance date.

In step S3520, a difference between the predicted mileage of the vehicle and the target mileage is calculated.

In step S3530, if the difference is less than or equal to the preset mileage threshold, a processing result of the vehicle executing the allocated train number task is obtained.

According to the embodiment of the invention, the maintenance plan mileage of the vehicle and the current actual mileage of the vehicle are obtained, and the running days of the current date from the maintenance date of the vehicle are obtained, so that the target mileage of the vehicle on the running days to be matched is obtained according to the maintenance plan mileage, the current actual mileage of the vehicle and the running days, and the difference value between the target mileage and the predicted mileage is calculated. And under the condition that the difference value is smaller than or equal to a preset mileage threshold value, a processing result that the vehicle can execute the allocated train number task can be obtained. And under the condition that the difference value is larger than the mileage threshold value, a processing result that the vehicle cannot execute the allocated train number task can be obtained, other train number tasks need to be allocated to the vehicle again, and the processing method of the embodiment is executed again until the vehicle is determined to execute the reallocated train number task.

The mileage threshold may be set according to a specific application scenario or application requirement, for example, may be set to 1 km.

In one embodiment, after performing step S3500, the processing method may further include: and displaying the processing result so as to carry out vehicle configuration.

Specifically, the electronic device itself that executes the processing method of the present embodiment may have a display screen, and after step S3500 is executed, the processing result is displayed on the electronic device. The electronic device executing the processing method of the embodiment may also send the processing result to other display devices (for example, may be a terminal device used by an operator) for display.

The operator can carry out the configuration processing on the vehicle according to the processing result. For example, in the case where the processing result is that the vehicle executes the allocated train number task, the train number task may be issued to the vehicle so that the vehicle executes the train number task. And under the condition that the processing result is that the vehicle does not execute the allocated train number task, the train number task can be allocated to the vehicle again until the processing result of the vehicle executing the allocated train number task is obtained.

In another embodiment of the present invention, steps S3200-S3400 may be executed for each vehicle number task corresponding to the vehicle in the vehicle and the vehicle number set to obtain a predicted mileage of the vehicle executing each corresponding vehicle number task. Then, step S3500 may further be: and obtaining a processing result of whether the vehicle executes each train task in the train number set according to the deviation of each predicted mileage and the target mileage. Specifically, an optimal train number task may be selected from the train number set according to the deviation between each predicted mileage and the target mileage, and then the processing result may be that the vehicle executes the optimal train number task, but does not execute other train number tasks.

The train number set includes all train number tasks in a train number list arranged for the operation day to be assembled, where the train number list may be a train number list for acquiring the operation day to be assembled, so that all train number tasks for acquiring the operation day to be assembled from the train number list form the train number set.

In one embodiment, the set of train numbers may be obtained from a terminal device at an operation site. The operator can input the train number list into the terminal device, and the electronic device can obtain the train number set according to the train number list.

In this embodiment, the train number task that meets the execution condition of the vehicle may be selected from the train number set as the train number task corresponding to the vehicle. Further, the vehicle number task meeting the execution condition of the vehicle may be selected according to the state information of the vehicle and the set constraint condition.

The state information of the vehicle may be determined from the vehicle information. In the case where the vehicle of the present embodiment is a train such as a subway, a train, a light rail, or the like, the above status information may include at least one of a parked track position, a parked track type.

The parked track position is used to indicate the parked position of the train within the yard track. For example, for a subway, each track has a track number, which may be a number or a letter, so long as it can distinguish different tracks, and is not limited herein. At most two trains can be parked on each track, the letters S and N can be used for distinguishing the north-south directions on the same track, for example, the track number for parking the vehicle is 23, and at the moment, the parked track position can be 23N, which indicates that the trains are parked in the north direction of the track with the track number of 23.

According to the method, the vehicle number tasks which can be allocated to the vehicle, namely the vehicle number tasks matched with the vehicle, are screened according to the state information of the vehicle, so that the vehicle number tasks which are basically impossible to be matched with the vehicle in the vehicle number collection can be filtered, and the efficiency and the accuracy of acquiring the vehicle number tasks corresponding to the vehicle are improved.

According to the deviation of each predicted mileage and the target mileage, the optimal train number task corresponding to the vehicle is obtained, and the optimal train number task executed by the vehicle can be specifically: and taking the train number task corresponding to the predicted mileage when the deviation is minimum as the optimal train number task.

For example, after the steps S3200 to S3400 are performed on the vehicle and each of the vehicle-time tasks including the first-time task, the second-time task, … … and the L-th-time task, the predicted mileage of the vehicle for performing the first-time task is S1, the predicted mileage of the vehicle for performing the second-time task is S2 and … …, and the difference between each predicted mileage and the mileage threshold S is calculated when the predicted mileage of the vehicle for performing the L-th-time task is SL. The difference between the predicted mileage S1 and the mileage threshold S is D1, the difference between the predicted mileage S2 and the mileage threshold S is D2, … …, the difference between the predicted mileage SL and the mileage threshold S is DL, and the differences D1 to DL are compared. If the difference D4 is minimal, then the predicted mileage when the difference D4 is the predicted mileage when the vehicle performs the fourth mission S4. Then, the fourth train number task may be determined to be the optimal train number task that the vehicle may perform.

According to the method of the embodiment, after the vehicle information and the vehicle number of tasks allocated to the vehicle are obtained, a mapping function between the feature vector and the driving mileage can be obtained by obtaining the selected feature vector, and the predicted mileage of the vehicle for executing the vehicle number of tasks is obtained according to the mapping function and the vector value of the feature vector, so that the processing result of whether the vehicle executes the vehicle number of tasks is obtained according to the predicted mileage. And further, the driving mileage of the vehicle can be executed according to the direction of planned maintenance, so that the mileage controllability of the vehicle is realized.

In one embodiment, after the step S3500 is performed, the vehicle information processing method may further include the following steps S3600 to S3700:

step S3600, after the vehicle executes the train number task, uses the vehicle and the train number task as a new training sample.

Step S3700, correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

According to the embodiment of the invention, after the vehicle executes the train number task, the actual mileage of the vehicle executing the train number task can be obtained, the vehicle and the train number task are used as new training samples, the mapping function is modified, namely the new training samples are added, and the mapping function is retrained, so that the mileage prediction is more and more accurate.

< example >

Fig. 8 is a method of processing vehicle information of one example, which may include the steps of:

in step S8100, vehicle information and a train number task assigned to the vehicle are acquired.

Step S8200, a selected feature vector is acquired.

Step S8310, obtaining training samples according to the historical operation data.

Step S8320, the mileage prediction expression of each training sample is determined by using the undetermined coefficient of the mapping function as a variable according to the vector value of the feature vector of each training sample.

In step S8330, for each training sample, a corresponding loss expression is determined according to the mileage prediction expression and the actual mileage.

In step S8340, the loss expression of each training sample is summed to obtain a loss function.

Step S8350, determining undetermined coefficients according to the loss function, and completing the training of the mapping function.

Step S8400, obtaining the predicted mileage of the vehicle to execute the train number task according to the mapping function and the vector value of the feature vector.

Step S8510, obtaining the maintenance schedule mileage of the vehicle and the current actual mileage of the vehicle.

Step S8520, the number of running days of the current date from the overhaul date of the vehicle is acquired.

Step S8530, obtaining the target mileage of the vehicle according to the maintenance schedule mileage, the current actual mileage and the running days.

In step S8610, a difference between the predicted mileage of the vehicle and the target mileage is calculated.

And step S8620, obtaining a processing result of the vehicle executing the allocated train number task under the condition that the difference value is smaller than or equal to a preset mileage threshold value.

< device example >

Fig. 9 is a functional block diagram of a processing device 9000 of vehicle information according to an embodiment of the present invention.

As shown in fig. 9, the processing device 9000 of the vehicle information of the present embodiment may include a vehicle task acquisition module 9100, a feature vector acquisition module 9200, a mapping function acquisition module 9300, an mileage prediction module 9400, and an execution determination module 9500. The vehicle task acquiring module 9100 is configured to acquire vehicle information and a vehicle number task allocated to a vehicle; the feature vector obtaining module 9200 is configured to obtain a selected feature vector, where the feature vector includes a plurality of features that affect mileage of a vehicle to perform a train number task, and the plurality of features includes a vehicle feature and a train number feature; the mapping function obtaining module 9300 is configured to obtain a mapping function between the feature vector and the driving range; the mileage predicting module 9400 is used to obtain the predicted mileage of the vehicle to execute the train number task according to the mapping function and the vector value of the characteristic vector; the execution determination module 9500 is configured to obtain a processing result of whether the vehicle executes the train number task according to the predicted mileage.

In one embodiment, the vehicle characteristics may include at least one of a vehicle number and a vehicle type; and/or the train number characteristics include at least one of departure time, arrival time, departure location, and arrival location.

In one embodiment, the plurality of features further includes a cross feature, wherein the cross feature is a feature that cross-correlates the vehicle with the train number task, the cross feature including a service task.

In one embodiment, the mapping function acquisition module 9300 is further configured to:

and training to obtain a mapping function according to the vector value of the feature vector of the training sample and the actual mileage corresponding to the training sample.

In this embodiment, training the mapping function may include:

determining a mileage prediction expression of each training sample by taking undetermined coefficients of the mapping function as variables according to vector values of feature vectors of each training sample;

and determining undetermined coefficients according to the loss function, and finishing the training of the mapping function.

In this embodiment, the step of constructing the loss function may include:

the loss expressions for each training sample are summed to obtain a loss function.

In this embodiment, the operation orchestration device 9000 may further include a new sample determination module and a function correction module (not shown in the figure). The new sample determining module is used for taking the vehicle and the train number task as new training samples after the vehicle executes the train number task; the function correction module is used for correcting the mapping function according to the vector value of the feature vector of the new training sample and the actual mileage of the new training sample on the corresponding operation day.

In this embodiment, the mapping function obtaining module 9300 may be further configured to:

and executing the training mapping function according to a preset training period.

In one embodiment, the execution determination module 9500 may also be configured to:

acquiring a target mileage of a vehicle;

calculating a difference between the predicted mileage and the target mileage;

In this embodiment, the step of obtaining the target mileage of the vehicle includes:

acquiring the maintenance schedule mileage of the vehicle and the current actual mileage of the vehicle;

and obtaining the target mileage of the vehicle according to the maintenance schedule mileage, the current actual mileage and the running days.

In one embodiment, the vehicle information processing apparatus further includes an event detection module (not shown in the figure) for detecting whether an event processing the vehicle information occurs. The vehicle task acquiring module 9100 is configured to acquire vehicle information and a train number task allocated to a vehicle when the event detecting module detects the occurrence of the event.

Further, the event includes at least any one or more of the following:

reaching the preset treatment time;

processing operation of receiving externally triggered vehicle information;

In one embodiment, the vehicle information processing apparatus may further include: and the display module (not shown in the figure) is used for displaying the processing result so as to carry out vehicle configuration.

< electronic device embodiment >

In this embodiment, there is also provided an electronic device 1000, where the electronic device 1000 may be the server 1100 shown in fig. 1 or the terminal device 1200 shown in fig. 2.

As shown in fig. 10, the electronic device 1000 may include a processing apparatus 9000 of vehicle information according to any embodiment of the present invention for implementing the processing method of vehicle information of any embodiment of the present invention.

In another embodiment, as shown in FIG. 11, the electronic device 1000 may further include a processor 1300 and a memory 1400, the memory 1400 for storing executable instructions; the processor 1300 is configured to operate the electronic device 1000 according to control of instructions to perform a method of processing vehicle information according to any embodiment of the present invention.

< computer-readable storage Medium >

In the present embodiment, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle information processing method according to any of the embodiments of the present invention.

The present invention may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method of processing vehicle information, comprising:

acquiring vehicle information and a train number task allocated to the vehicle;

obtaining a selected feature vector, wherein the feature vector comprises a plurality of features affecting mileage of the vehicle to perform the train number task, the plurality of features comprising a vehicle feature and a train number feature;

Obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage;

the step of obtaining the mapping function between the feature vector and the driving mileage comprises the following steps:

2. The processing method of claim 1, wherein the vehicle characteristics include at least one of a vehicle number and a vehicle type; and/or the train number characteristics include at least one of departure time, arrival time, departure location, and arrival location.

3. The processing method of claim 1, wherein the plurality of features further comprises a cross feature, wherein the cross feature is a feature that cross-correlates a vehicle with the train number task, the cross feature comprising a service task; the overhaul task comprises at least one of the following: inspection tasks, repair tasks, maintenance tasks, cleaning tasks.

4. The processing method according to claim 1, wherein the training results in the mapping function, comprising:

5. The processing method of claim 4, wherein the constructing a loss function comprises:

6. The processing method according to claim 1, wherein the processing method further comprises:

7. The processing method according to claim 1, wherein the processing method further comprises:

8. The processing method according to claim 1, wherein the step of obtaining a processing result of whether the vehicle performs the train number task based on the predicted mileage includes:

acquiring a target mileage of the vehicle;

calculating a difference between the predicted mileage and the target mileage;

9. The method of processing of claim 8, wherein the step of obtaining the target mileage of the vehicle comprises:

10. The processing method according to claim 1, wherein the vehicle information processing method further comprises:

Detecting whether an event of processing vehicle information occurs;

11. The processing method of claim 10, wherein the event comprises at least any one or more of:

reaching the preset treatment time;

processing operation of receiving externally triggered vehicle information;

12. The processing method according to claim 1, wherein the vehicle information processing method further comprises:

and displaying the processing result so as to carry out vehicle configuration.

13. A processing apparatus of vehicle information, comprising:

a feature vector acquisition module configured to acquire a selected feature vector, where the feature vector includes a plurality of features affecting mileage of the vehicle to perform the train number task, the plurality of features including a vehicle feature and a train number feature;

the execution determining module is used for obtaining a processing result of whether the vehicle executes the train number task or not according to the predicted mileage;

wherein, the mapping function obtaining module is further configured to:

14. An electronic device comprising the processing apparatus of claim 13; alternatively, the system comprises a memory for storing executable instructions and a processor; the processor is configured to execute the processing method according to any one of claims 1 to 12 according to control of the instruction.

15. A computer readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the processing method according to any of claims 1 to 12.